document_id string | document_text string | document_filename string | document_metadata dict | document_summary string | summarization_model string | chunks list | multihop_chunks list |
|---|---|---|---|---|---|---|---|
03b818b8-4123-4b12-8113-6a14cf5522e5 | "5\n2\n0\n2\n\nr\np\nA\n2\n\n]\nL\nC\n.\ns\nc\n[\n\n1\nv\n3\n3\n8\n1\n0\n.\n4\n0\n5\n2\n:\nv\ni\nX\n(...TRUNCATED) | yourbench_arxiv_paper.md | {
"file_size": 133539
} | Qwen/Qwen2.5-72B-Instruct | [{"chunk_id":"03b818b8-4123-4b12-8113-6a14cf5522e5_0","chunk_text":"5\n2\n0\n2\n\nr\np\nA\n2\n\n]\nL(...TRUNCATED) | [{"chunk_ids":["03b818b8-4123-4b12-8113-6a14cf5522e5_3","03b818b8-4123-4b12-8113-6a14cf5522e5_4"],"c(...TRUNCATED) |
My Custom Benchmark
This dataset was generated using YourBench (v0.6.0), an open-source framework for generating domain-specific benchmarks from document collections.
Pipeline Steps
- ingestion: Read raw source documents, convert them to normalized markdown and save for downstream steps
- summarization: Perform hierarchical summarization: chunk-level LLM summaries followed by combine-stage reduction
- chunking: Split texts into token-based single-hop and multi-hop chunks
- single_shot_question_generation: Generate standalone question-answer pairs per chunk using LLM
- multi_hop_question_generation: Generate multi-hop QA pairs requiring reasoning across multiple chunks
- lighteval: Merge QA pairs and chunk metadata into a lighteval compatible dataset for quick model-based scoring
Reproducibility
To reproduce this dataset, use YourBench v0.6.0 with the following configuration:
hf_configuration:
hf_dataset_name: my_custom_benchmark
hf_organization: Tooru-Tooru
hf_token: $HF_TOKEN
local_dataset_dir: data/saved_dataset
jsonl_export_dir: data/jsonl_export
pipeline_config:
ingestion:
source_documents_dir: data/raw
output_dir: data/processed
pdf_llm_prompt: yourbench/prompts/ingestion/pdf_llm_prompt.md
summarization:
summarization_user_prompt: yourbench/prompts/summarization/summarization_user_prompt.md
combine_summaries_user_prompt: yourbench/prompts/summarization/combine_summaries_user_prompt.md
chunking: {}
single_shot_question_generation:
additional_instructions: Generate questions to test a curious adult
single_shot_system_prompt: yourbench/prompts/question_generation/single_shot_system_prompt.md
single_shot_system_prompt_multi: yourbench/prompts/question_generation/single_shot_system_prompt_multi.md
single_shot_user_prompt: yourbench/prompts/question_generation/single_shot_user_prompt.md
multi_hop_question_generation:
additional_instructions: Generate questions to test a curious adult
multi_hop_system_prompt: yourbench/prompts/question_generation/multi_hop_system_prompt.md
multi_hop_system_prompt_multi: '<custom_prompt: # Multi-Hop Document Comprehension
Question Genera...>'
multi_hop_user_prompt: yourbench/prompts/question_generation/multi_hop_user_prompt.md
lighteval: {}
model_list:
- model_name: Qwen/Qwen2.5-VL-72B-Instruct
api_key: $API_KEY
max_concurrent_requests: 32
encoding_name: cl100k_base
provider: hf-inference
- model_name: Qwen/Qwen2.5-72B-Instruct
api_key: $API_KEY
max_concurrent_requests: 32
encoding_name: cl100k_base
provider: novita
model_roles:
ingestion:
- Qwen/Qwen2.5-VL-72B-Instruct
summarization:
- Qwen/Qwen2.5-72B-Instruct
chunking:
- intfloat/multilingual-e5-large-instruct
single_shot_question_generation:
- Qwen/Qwen2.5-72B-Instruct
multi_hop_question_generation:
- Qwen/Qwen2.5-72B-Instruct
question_generation:
- Qwen/Qwen2.5-VL-72B-Instruct
cross_document_question_generation:
- Qwen/Qwen2.5-VL-72B-Instruct
question_rewriting:
- Qwen/Qwen2.5-VL-72B-Instruct
prepare_lighteval:
- Qwen/Qwen2.5-VL-72B-Instruct
lighteval:
- Qwen/Qwen2.5-VL-72B-Instruct
citation_score_filtering:
- Qwen/Qwen2.5-VL-72B-Instruct
(This dataset card was automatically generated by YourBench)
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